Modeling the effect of patient choice on the performance of open access scheduling

Abstract

Open access scheduling, introduced in recent years, is a revolutionary concept for improving healthcare access and reducing patient no-shows. In this paper, a Markov chain model is presented to capture appointment scheduling in open access clinics when considering patient choice of appointments. Due to the curse of dimensionality, it is impossible to solve the steady-state distribution of the Markov chain model for a typical-size open access primary care clinic. Therefore, an approximate approach is proposed to efficiently estimate the performance of a provider capacity policy based on the Markov chain model. This approach can accurately estimate the performance of a practical capacity policy in a significantly shorter time. Using this approach, the impact of patient choice on the performance of provider capacity policies is investigated.